Figure 5
rstudio
September 10, 2024 13:19:17 CEST
Introduction
Load libraries
library(Seurat)
library(canceRbits)
library(dplyr)
library(patchwork)
library(DT)
library(SCpubr)
library(tibble)
library(dittoSeq)
library(scRepertoire)
library(reshape2)
library(viridis)
library(tidyr)
library(grDevices)
library(ggpubr)
library(ggplot2)
library(ggnewscale)
library(RColorBrewer)
library(scales)
library(enrichplot)
library(clusterProfiler)
library("org.Hs.eg.db")
library(DOSE)
library(msigdbr)
library(stringr)path to data
# path to data, to be adapted - serve here to get raw data
path_to_data = file.path("mnt_data","BCC_2023-06/biomedical-sequencing.at/projects/BSA_0460_MF_BCC_1ab413522d3e4d2d959b1b8f2681cf06/COUNT/")
# Load seurat object containing single cell pre-processed and annotated data, in the metadata folder
srat <- readRDS( "20241005_Ressler2024_NeoBCC.Rds")
meta <- srat@meta.data
meta$WHO <- "SD"
meta$WHO[meta$patient %in% c("NeoBCC007_post", "NeoBCC008_post", "NeoBCC012_post", "NeoBCC017_post")] <- "CR"
meta$WHO[meta$patient %in% c("NeoBCC004_post", "NeoBCC006_post", "NeoBCC010_post", "NeoBCC011_post")] <- "PR"
srat <- AddMetaData(srat, meta$WHO, col.name = "WHO")
srat$WHO <- factor(srat$WHO, levels = c("CR", "PR", "SD"))colors
colors_B <- c(
"34" = "#ae017e",
"7" = "#377eb8",
"21" = "#f781bf",
"38" = "#fed976",
"15" = "#a6cee3" ,
"22" = "#e31a1c" ,
"3" = "#9e9ac8",
"4" = "#fdb462" ,
"24" = "#b3de69"
)
colors_clonotype = c("Small (1e-04 < X <= 0.001)" = "#8c6bb1",
"Medium (0.001 < X <= 0.01)" = "#41b6c4",
"Large (0.01 < X <= 0.1)" = "#fec44f",
"Hyperexpanded (0.1 < X <= 1)" = "#ce1256"
)
colors_Ig = c("IGHA1" = "#addd8e",
"IGHA2" = "#238443",
"IGHM" = "#dd3497" ,
"IGHD" = "#41b6c4",
"IGHG1" = "#fec44f",
"IGHG2" = "#fe9929",
"IGHG3" = "#ec7014",
"IGHG4" = "#fee391"
)Figure 5a
s <- subset(srat, subset = anno_l1 %in% c("B cells","Plasma cells"))
s@meta.data$seurat_clusters <- factor(s@meta.data$seurat_clusters, levels=c("34","7", "21", "38",
"15", "22","3", "4", "24"))
p <- SCpubr::do_DimPlot(sample = s,
reduction = "umap",
label = TRUE,
repel = TRUE,
label.color = "black",
legend.position = "none",
group.by = "seurat_clusters",
font.size = 25,
pt.size = 0.5,
colors.use = colors_B) + theme_minimal() + NoLegend() + theme(text = element_text(size=20))
p
Figure 5b
Idents(s) <- s$seurat_clusters
genes <- list( "B" = c("MS4A1", "CD19"),
"Ag presentation" = c("HLA-DRA", "HLA-DRB1", "HLA-DPB1", "CD40"),
"Cytotox" = c("GZMK", "PRF1"),
"Mem" = "CD27",
"Plasma" = c("IGKC", "IGHG1", "CD38", "SDC1", "JCHAIN"),
"Cell state" = c("G2M.Score", "S.Score","percent.mito")
)
p <- SCpubr::do_DotPlot(sample = s,
features = genes,
font.size = 18,
legend.length = 12,
legend.type = "colorbar",
dot.scale = 8,
colors.use = c("#7fbc41","#b8e186", "#f7f7f7","#fde0ef", "#c51b7d"),
use_viridis = FALSE
)
p
Figure 5c
q1 <- SCpubr::do_BarPlot( s,
group.by = "seurat_clusters",
split.by = "WHO",
position = "fill",
flip = TRUE,
order=FALSE,
legend.position = "none",
font.size = 32 ,
colors.use = colors_B,
xlab = "",
ylab = ""
) + xlab("Response") + ylab("% of B and plasma cells")
q1
DT::datatable(q1$data,
caption = ("Figure 5c"),
extensions = 'Buttons',
options = list( dom = 'Bfrtip',
buttons = c( 'csv', 'excel')))q1 <- SCpubr::do_BarPlot( s,
group.by = "seurat_clusters",
split.by = "Pathological.Response",
position = "fill",
flip = TRUE,
order=FALSE,
legend.position = "none",
font.size = 32 ,
colors.use = colors_B,
xlab = "",
ylab = ""
) + xlab("Response") + ylab("% of B and plasma cells")
q1
Open and pre-process clonotyoe data
combined <- readRDS("Ressler2024/metadata/100_CombinedBCR.Rds")
srat <- RenameCells(srat,new.names = paste0(srat$patient, "_",gsub("(_1|_2|_3|_4|_5|_6|_7|_8|_9)*", "",colnames(srat))))
srat <- RenameCells(srat,new.names = gsub("_post", "",colnames(srat)))
srat$sample <- gsub("_post", "",srat$patient)
seurat <- combineExpression(combined, srat,
cloneCall = "strict",
group.by = "sample", chain = "both",
proportion = TRUE,
filterNA = TRUE)
s <- subset(seurat, subset = anno_l1 %in% c("B cells","Plasma cells") )
#s@meta.data$anno_l1 <- droplevels(s@meta.data$anno_l1)
#s@meta.data$seurat_clusters <- droplevels(s@meta.data$seurat_clusters)
s@meta.data$cloneType <- factor(s@meta.data$cloneType, levels = unique(s$cloneType))Figure 5d
done on the highly confident B and plasma cells with exactly 2 IGH and IGL chains
df <- s@meta.data
df <- df %>%
separate( CTgene, c("IGH", "IGL"), sep = "_") %>%
separate( IGH, c("V", "D", "J", "C"), "\\.")
s <- AddMetaData(s, df$C, col.name = "Ig_level")
sss <- subset(s, subset = Ig_level != "NA")
dim(sss)## [1] 27974 13045sss@meta.data$cloneType <- factor(sss@meta.data$cloneType, levels = c("Hyperexpanded (0.1 < X <= 1)", "Large (0.01 < X <= 0.1)", "Medium (0.001 < X <= 0.01)", "Small (1e-04 < X <= 0.001)"))
# define cell group membership
Idents(sss) <- sss$cloneType
sss@meta.data$Ig_level <- factor(sss@meta.data$Ig_level, levels = c("IGHA1",
"IGHA2",
"IGHM",
"IGHD",
"IGHG1",
"IGHG2",
"IGHG3",
"IGHG4"))
q1 <- SCpubr::do_BarPlot( sss,
group.by = "Ig_level",
split.by = "anno_l1",
position = "fill",
flip = TRUE,
order=FALSE,
legend.position = "right",
font.size = 30 ,
colors.use = colors_Ig) +
xlab("") +
ylab("Frequency of Ig isotypes")
q1
DT::datatable(q1$data,
caption = ("Figure 5d"),
extensions = 'Buttons',
options = list( dom = 'Bfrtip',
buttons = c( 'csv', 'excel')))q1 <- SCpubr::do_BarPlot( sss,
group.by = "Ig_level",
split.by = "cloneType",
position = "fill",
flip = TRUE,
order=FALSE,
legend.position = "right",
font.size = 30 ,
colors.use = colors_Ig ) +
xlab("") +
ylab("Frequency of Ig isotypes")
q1
Figure 5f
Idents(s) <- s$seurat_clusters
s@meta.data$seurat_clusters <- factor(s@meta.data$seurat_clusters, levels=c("34","7", "21", "38",
"15", "22","3", "4", "24"))
s@meta.data$cloneType <- factor(s@meta.data$cloneType, levels = c("Hyperexpanded (0.1 < X <= 1)", "Large (0.01 < X <= 0.1)", "Medium (0.001 < X <= 0.01)", "Small (1e-04 < X <= 0.001)"))
# define cell group membership
Idents(s) <- s$cloneType
de_markers <- DElegate::FindAllMarkers2(s, replicate_column = "patient", method = "edger", min_fc = 0, min_rate = 0.1)
signature_h <- de_markers$feature[de_markers$group1=="Hyperexpanded (0.1 < X <= 1)" ]
background <- rownames(s)
library(msigdbr)
library(clusterProfiler)
# enricher for cnet plot
GO_H_gene_sets = msigdbr(species = "human", category = "H")
msigdbr_t2g = GO_H_gene_sets %>% dplyr::distinct(gs_name, gene_symbol) %>% as.data.frame()
ego_h <- enricher(gene = signature_h, universe = background, TERM2GENE = msigdbr_t2g)
b <- barplot(ego_h, title = "Hallmarks´enrichment of hyperexpanded clones")
tmp <- b$data
tmp$ID <- str_trunc(tmp$ID, 100, "right")
tmp$ID <- factor(tmp$ID, levels = tmp$ID[order(tmp$Count)])
p <- ggplot(tmp, aes(Count, ID)) +
geom_bar(stat = "identity", color="black", fill = colors_clonotype["Hyperexpanded (0.1 < X <= 1)"]) +
theme_classic()+
geom_text(
aes(label = (paste( "padj=", format(p.adjust,scientific = TRUE, digits = 2)))),
color = "black",
size = 6,
hjust=1,
position = position_dodge(0.5) ) +
theme(text = element_text(size=18)) +
ggtitle("Hyperexpanded clones")
p